Two-sided Cumulative Distribution Function

Cumulative Distribution Function for positive and negative residuals.

plot_tsecdf(
  object,
  ...,
  scale_error = TRUE,
  outliers = NA,
  residuals = TRUE,
  reverse_y = FALSE
)

plotTwoSidedECDF(
  object,
  ...,
  scale_error = TRUE,
  outliers = NA,
  residuals = TRUE,
  reverse_y = FALSE
)

Arguments

object	An object of class 'auditor_model_residual' created with model_residual function.
...	Other modelAudit objects to be plotted together.
scale_error	A logical value indicating whether ECDF should be scaled by proportions of positive and negative proportions.
outliers	Number of outliers to be marked.
residuals	A logical value indicating whether residuals should be marked.
reverse_y	A logical value indicating whether values on y axis should be reversed.

Value

A ggplot object.

Examples

dragons <- DALEX::dragons[1:100, ]

# fit a model
model_lm <- lm(life_length ~ ., data = dragons)

lm_audit <- audit(model_lm, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated
#>   -> model label       :  lm  (  default  )
#>   -> data              :  100  rows  8  cols 
#>   -> target variable   :  100  values 
#>   -> predict function  :  yhat.lm  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package stats , ver. 4.1.1 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  585.8311 , mean =  1347.787 , max =  2942.307  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -88.41755 , mean =  -1.489291e-13 , max =  77.92805  
#>   A new explainer has been created!  

# validate a model with auditor
mr_lm <- model_residual(lm_audit)
plot_tsecdf(mr_lm)
plot(mr_lm, type="tsecdf")

library(randomForest)
model_rf <- randomForest(life_length~., data = dragons)
rf_audit <- audit(model_rf, data = dragons, y = dragons$life_length)
#> Preparation of a new explainer is initiated
#>   -> model label       :  randomForest  (  default  )
#>   -> data              :  100  rows  8  cols 
#>   -> target variable   :  100  values 
#>   -> predict function  :  yhat.randomForest  will be used (  default  )
#>   -> predicted values  :  No value for predict function target column. (  default  )
#>   -> model_info        :  package randomForest , ver. 4.6.14 , task regression (  default  ) 
#>   -> predicted values  :  numerical, min =  751.7767 , mean =  1344.281 , max =  2452.94  
#>   -> residual function :  difference between y and yhat (  default  )
#>   -> residuals         :  numerical, min =  -185.0481 , mean =  3.506103 , max =  445.4805  
#>   A new explainer has been created!  
mr_rf <- model_residual(rf_audit)
plot_tsecdf(mr_lm, mr_rf, reverse_y = TRUE)